most citedPrincipal component analysis based clustering for high-dimension, low-sample-size data

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stat.ML2017★ 2 cited

Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models

Makoto Aoshima, Kazuyoshi Yata

We consider classifiers for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We first show that high-dimensional data often have the SSE model. We consider a…

stat.ML2017

Support vector machine and its bias correction in high-dimension, low-sample-size settings

Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima

In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that the hard-margin linear SVM ho…